A dataset of lung ultrasound images for automated AI-based lung disease classification

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-06 DOI:10.1016/j.dib.2025.112034

Andrew Katumba , Sudi Murindanyi , Nixson Okila , Joyce Nakatumba-Nabende , Cosmas Mwikirize , Jonathan Serugunda , Samuel Bugeza , Anthony Oriekot , Juliet Bossa , Eva Nabawanuka

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引用次数: 0

Abstract

Lung ultrasound (LUS) is increasingly recognized as a valuable imaging modality for evaluating various pulmonary conditions. Despite its clinical utility, accurate interpretation of LUS remains challenging due to factors such as inter-operator variability, dependence on sonographer expertise, and inherently low signal-to-noise ratios. This article presents a curated benchmark dataset of labelled LUS images acquired in Uganda, intended to support the development of automated, AI-based diagnostic tools for lung disease classification. The dataset comprises 1062 labelled images collected from patients at Mulago National Referral Hospital and Kiruddu Referral Hospital by senior radiologists. The dataset is suitable for training and evaluating convolutional neural network-based models and is expected to facilitate research in developing robust deep learning systems for pulmonary disease diagnosis using LUS.

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用于基于人工智能的肺部疾病自动分类的肺部超声图像数据集

肺超声（LUS）越来越被认为是评估各种肺部疾病的一种有价值的成像方式。尽管LUS具有临床应用价值，但由于操作人员之间的差异、对超声医师专业知识的依赖以及固有的低信噪比等因素，准确解释LUS仍然具有挑战性。本文介绍了在乌干达获得的标记LUS图像的经过整理的基准数据集，旨在支持开发用于肺部疾病分类的自动化、基于人工智能的诊断工具。该数据集包括由资深放射科医生从穆拉戈国家转诊医院和基鲁杜转诊医院的患者收集的1062张标记图像。该数据集适用于训练和评估基于卷积神经网络的模型，并有望促进使用LUS开发鲁棒深度学习系统用于肺部疾病诊断的研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.